Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Discovering Relevance-Dependent Bicluster Structure from Relational Data
Authors: Iku Ohama, Takuya Kida, Hiroki Arimura
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the R-IB extracts more essential bicluster structure with better computational efficiency than conventional models. We present experimental results obtained using real-world datasets. |
| Researcher Affiliation | Collaboration | Iku Ohama, Panasonic Corporation, Japan... Takuya Kida, and Hiroki Arimura, Graduate School of Information Science and Technology, Hokkaido University, Japan. |
| Pseudocode | No | The paper describes the inference process for the R-IB model in prose and mathematical equations, but it does not provide explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The first dataset was the Animal [Osherson et al., 1991] dataset... The second dataset was the Enron [Klimat and Yang, 2004] dataset... The final dataset was the Movie Lens [MOV, as of 2003] dataset... |
| Dataset Splits | Yes | All scores were calculated using 10-fold cross validation, and the overall average and deviation were reported. |
| Hardware Specification | Yes | All the models were implemented in JAVA and run on a PC with an Intel R Xeon R 2.7 GHz CPU. |
| Software Dependencies | No | The paper states 'All the models were implemented in JAVA' but does not provide specific version numbers for Java or any other software dependencies. |
| Experiment Setup | Yes | In all the experiments, we also fit all hyperparameters of both the proposed and baseline models assuming the same gamma priors (Gamma(1.0,1.0)). We ran 4000 Gibbs iterations for each model on each dataset and used the final 500 iterations to calculate the measurement. |